Results 21 to 30 of about 6,440,632 (323)
Making predictive modelling ART: accurate, reliable, and transparent
Ecosphere, 2020 Models are increasingly being used for prediction in ecological research. The ability to generate accurate and robust predictions is necessary to help respond to ecosystem change and to further scientific research.
Korryn Bodner +2 more
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Model Fit and Model Selection [PDF]
Review, 2007 This paper uses an example to show that a model that fits the available data perfectly may pro vide worse answers to policy questions than an alternative, imperfectly fitting model. The author argues that, in the context of Bayesian estimation, this result can be interpreted as being due to the use of an inappropriate prior over the parameters of shock
openaire +2 more sources
Information criteria for astrophysical model selection [PDF]
, 2007 Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information ...
Liddle, Andrew R
core +2 more sources
Risks, 2023 Stochastic mortality models seek to forecast future mortality rates; thus, it is apparent that the objective variable should be the mortality rate expressed in the original scale. However, the performance of stochastic mortality models—in terms, that is,
Miguel Santolino
doaj +1 more source
Computational Nosology and Precision Psychiatry [PDF]
Computational Psychiatry, 2017 This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms ...
Karl J. Friston +2 more
doaj +3 more sources
Frontiers in Veterinary Science, 2017 Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial ...
Anke Hüls +8 more
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Optimal predictive model selection
, 2004 Often the goal of model selection is to choose a model for future prediction, and it is natural to measure the accuracy of a future prediction by squared error loss.
Barbieri, Maria Maddalena +1 more
core +1 more source
Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval
Remote Sensing, 2018 Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality ...
Katalin Blix, Torbjørn Eltoft
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Source Model Selection for Deep Learning in the Time Series Domain
IEEE Access, 2020 Transfer Learning aims to transfer knowledge from a source task to a target task. We focus on a situation when there is a large number of available source models, and we are interested in choosing a single source model that can maximize the predictive ...
Amiel Meiseles, Lior Rokach
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ComputationPredicting corporate bankruptcy is a key task in financial risk management, and selecting a machine learning model with superior generalization performance is crucial for prediction accuracy.
Vlad Teodorescu +1 more
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